Adverse drug reactions
10.5124/jkma.2019.62.9.472
- Author:
Min Kyung CHO
1
;
Dong Yoon KANG
;
Hye Ryun KANG
Author Information
1. Drug Safety Center, Seoul National University Hospital, Korea. helenmed@snu.ac.kr
- Publication Type:Original Article
- Keywords:
Drug-related side effects and adverse reactions;
Pharmacovigilance;
Adverse drug reaction reporting systems;
Pharmacogenetics;
Big data
- MeSH:
Adverse Drug Reaction Reporting Systems;
Drug Hypersensitivity;
Drug-Related Side Effects and Adverse Reactions;
Humans;
Individuality;
Medical Informatics;
Pharmacogenetics;
Pharmacology;
Pharmacovigilance
- From:Journal of the Korean Medical Association
2019;62(9):472-479
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
There are no drugs without the risk of potential adverse reactions. All pharmacologically active substances can cause adverse drug reactions (ADRs). This paper aims at introducing recent trends in pharmacosurveillance systems for ADRs, which can be broadly classified into type A and B reactions. Since type A reactions are associated with drug pharmacology, they are usually dose-dependent and predictable. Whereas, type B reactions occur in some susceptible individuals, regardless of the pharmacological action of drug. Drug hypersensitivity reactions are typical examples of type B reactions and are subclassified according to the underlying pathomechanism. Recent advancements in pharmacogenomics have enlightened the understanding of individual differences in drug efficacy and susceptibility to ADRs. Therefore, expectations for safe personalized medicines are higher than ever before. However, premarketing clinical trials are too small and too short to uncover rare but serious ADRs and detect long-standing ADRs. In the past, post-marketing surveillance systems mainly focused on passive ADR monitoring systems, based on spontaneous reports. Recently, the importance of active pharmacovigilance systems, which use big data, is growing with recent advancements in medical informatics. Thus, regarding ADRs, suspecting and detecting the causative drug using causality assessment based on data science may contribute to decrease suffering induced by ADRs.
